The best design with the least amount of effort

A 45' webinar recording, explaining how machine learning helps resolve engineering simulation bottlenecks.

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Engineering simulation in a structured way

Noesis Solutions’ engineering workflow automation enables engineering teams to set up and run simulation campaigns in a structured way, formalizing best practices into a well-documented and repeatable process. Rather than launching one particular instance of an engineering simulation workflow, development teams can use Design of Experiments to set up a sensibly planned series of experiments and get valuable insights into the design space.

Enabling highly accurate predictions fast

But when dealing with resource intensive engineering simulations, sophisticated machine learning solutions are becoming increasingly important to speed up the entire process. Statistical learning techniques enable computers to learn, grow, change, and develop by themselves when exposed to new data. As machine learning can discover and display patterns buried in engineering simulation data, it has great potential to deliver highly accurate predictions in a short time and using a limited number of simulation experiments.


  • the state-of-the-art machine learning approaches which will be made available in the upcoming version of id8 discover - including Random Forest Regression and Relevance Vector Regression approaches;
  • how these methods can be utilized as stand-alone black-box intelligence tools, or integrated with optimizers to reach the best design with least computational effort;
  • how these machine learning models can transform bottlenecks with resource intensive CAE/FEA/CFD simulations in many different ways.

Find out much more

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